def compute_free_residual(sim, index, calib_data, condition='free'):
Cdata = load_dict(load_dict('data/best_chelatedZn_config.npz')['filename'])
try:
sim_data = load_dict(
os.path.join('data', 'calib',
sim.params_filename(index) + '.npz'))
Residual = 1.
for cond in ['20Hz_protocol', '3Hz_protocol']:
tcond = (sim_data['t']>(sim_data['%s_tstart' % cond]-calib_data['DT0_%s' % cond])) &\
(sim_data['t']<sim_data['%s_tstart' % cond]-calib_data['DT0_%s' % cond]+\
calib_data['DTfull_%s' % cond])
# normalizing to peak in the exp
first_peak_cond = (calib_data['t_%s' % cond] <
calib_data['DT0_%s' % cond] + 50)
# exp data
trace_exp = calib_data['Iexp_freeZn_%s' % cond]
trace_exp0 = calib_data['Iexp_chelatedZn_%s' % cond]
# model data
trace_model = -1e3 * (
sim_data['Ic'][tcond] - sim_data['Ic'][tcond][0]) # - sign
trace_model0 = -1e3 * (Cdata['Ic'][tcond] - Cdata['Ic'][tcond][0])
# normalizing to peak
factor_model = np.max(trace_model[first_peak_cond])
factor_model0 = np.max(trace_model0[first_peak_cond])
# trace_model0 *= factor_model/factor_model0 # norm to peak, MAX
trace_model /= factor_model
trace_model0 /= factor_model0
factor_exp = np.max(trace_exp[first_peak_cond])
factor_exp0 = np.max(trace_exp0[first_peak_cond])
trace_exp /= factor_exp
trace_exp0 /= factor_exp0
diff_model = trace_model0 - trace_model
diff_exp = trace_exp0 - trace_exp
Residual *= 1 + np.mean(
np.abs(diff_model - diff_exp)) / np.std(diff_exp)
except FileNotFoundError:
print(sim.params_filename(index) + '.npz', 'not found')
Residual = 1e10
return Residual
def load_data(self, filename):
data = load_dict(filename)
self.params = {}
for key, val in data.items():
if key in self.keys_for_saving():
setattr(self, key, val)
else:
self.params[key] = val
def plot_sim(filename, ge,
omf_data=None,
Umodel_data=None,
mf_data=None):
## load file
data = ntwk.load_dict_from_hdf5(filename)
# ## plot
fig, AX = ntwk.activity_plots(data,
smooth_population_activity=10,
COLORS=COLORS,
pop_act_log_scale=True)
if Umodel_data is not None:
AX[2].plot(Umodel_data['t'],
Umodel_data['desired_Vm'], '-', lw=2, color='dimgrey', label='mean-field')
if mf_data is None:
mf = ntwk.FastMeanField(data, tstop=6., dt=2.5e-3, tau=20e-3)
mf.build_TF_func(tf_sim_file='neural_network_dynamics/theory/tf_sim_points.npz')
X, mVm, sVm = mf.run_single_connectivity_sim(mf.ecMatrix, verbose=True)
t = np.linspace(0, data['tstop'], X.shape[1])
for i, label in enumerate(data['REC_POPS']):
AX[-1].plot(t, 1e-2+X[i,:], '--', lw=1, color=COLORS[i])
ge.plot(t, 1e3*mVm[i,:], sy=1e3*sVm[i,:], color='k', lw=1, label='mean-field', ax=AX[2+i])
AX[2+i].plot(t, 1e3*(mVm[i,:]-sVm[i,:]), 'k-', lw=0.5)
AX[2+i].plot(t, 1e3*(mVm[i,:]+sVm[i,:]), 'k-', lw=0.5)
# AX[2].plot(mf_data['t'], mf_data['desired_Vm'], 'k--', lw=2, label='U-model')
# AX[2].legend(frameon=False, loc='best')
if omf_data is None:
try:
omf_data = load_dict(filename.replace('ntwk.h5', 'mf.npz'))
except FileNotFoundError:
pass
if omf_data is not None:
t = np.linspace(0, data['tstop'], len(omf_data['pyrExc']))
for i, label in enumerate(data['REC_POPS']):
AX[-1].plot(t, 1e-2+omf_data[label], '-', lw=4, color=COLORS[i], alpha=.5)
# AX[-1].plot(t, omf_data[label], 'k--')
figM, _, _ = plot_matrix(data)
ntwk.show()
def compute_chelated_residual(sim, index, calib_data, condition='chelated'):
try:
sim_data = load_dict(
os.path.join('data', 'calib',
sim.params_filename(index) + '.npz'))
Residual = 1.
for cond in ['20Hz_protocol', '3Hz_protocol']:
tcond = (sim_data['t']>(sim_data['%s_tstart' % cond]-calib_data['DT0_%s' % cond])) &\
(sim_data['t']<sim_data['%s_tstart' % cond]-calib_data['DT0_%s' % cond]+\
calib_data['DTfull_%s' % cond])
trace_model = -1e3 * (
sim_data['Ic'][tcond] - sim_data['Ic'][tcond][0]) # - sign
trace_exp = calib_data['Iexp_chelatedZn_%s' % cond]
Residual *= 1 + np.sum((trace_model - trace_exp)**2) / np.sum(
(trace_exp)**2)
except FileNotFoundError:
print(sim.params_filename(index) + '.npz', 'not found')
Residual = 1e10
return Residual
def demo_plot():
fig, AX = ge.figure(axes=(1, 2), figsize=(2, 1))
for key, color, l in zip(
['AMPA-only', 'chelated-Zinc', 'free-Zinc'], [ge.blue, 'k', ge.green],
['$g_{AMPA}$', '$g_{NMDA}$ (chelated)', '$g_{NMDA}$ (free)']):
data = load_dict('data/nmda-spike/demo-%s.npz' % key)
AX[0].plot(data['t'], data['Vm_soma'], color=color, label=key)
AX[1].plot(data['t'], data['gNMDA_syn'], color=color, label=l + ' ')
for e, n in zip(data['events'], data['NSYNs']):
ge.annotate(AX[1],
' $N_{syn}$=%i' % n, (e, 0),
xycoords='data',
rotation=90,
ha='right',
size='xx-small')
ge.legend(AX[0], size='x-small')
ge.legend(AX[1], size='xx-small', ncol=3)
ge.set_plot(AX[0], ylabel='$V_m$ (mV)')
ge.set_plot(AX[1], ylabel='$g$ (nS)', xlabel='time (ms)')
}
if not os.path.isfile('data/draft_mf_result.npz') or\
input('Do you want to peform again the mean-field simulation ? [y/N]\n')=='y':
print('performing calculus [...]')
X = ntwk.mean_field.solve_mean_field_first_order(
Model,
DYN_SYSTEM,
INPUTS=INPUTS,
CURRENT_INPUTS=CURRENT_INPUTS,
dt=dt,
tstop=tstop)
np.savez('data/draft_mf_result.npz', **X)
else:
X = load_dict('data/draft_mf_result.npz')
fig, AX = ntwk.activity_plots(data,
COLORS=COLORS,
smooth_population_activity=10,
pop_act_log_scale=True)
for i, pop in enumerate(REC_POPS):
AX[-1].plot(1e3 * t, 1e-2 + X[pop], color=COLORS[i], lw=3, alpha=0.6)
um = Umodel()
ge.plot(um.predict_Vm(1e-3 * t_sim, data['Rate_AffExc_pyrExc']),
fig_args={'figsize': (3, 1)})
ge.show()
'tauRiseNMDA': 3,# [ms], Farinella et al., 2014
'tauDecayNMDA': 100,# [ms] --- Destexhe et al.:. 25-125ms, Farinella et al., 2014: 70ms
'cMg': 1., # mM
'etaMg': 0.33, # 1/mM
'V0NMDA':1./0.08,# [mV]
'Mg_NMDA':1.,# mM
}
if sys.argv[-1]=='plot':
from analyz.IO.npz import load_dict
from datavyz import ges as ge
data = load_dict('data.npz')
fig, AX = ge.figure(axes=(1,3), figsize=(2.,1.))
ge.plot(data['t'], Y=[data['Vsoma'], data['Vdend']],
LABELS=['soma', 'dend'], COLORS=['k', ge.blue], ax=AX[0],
axes_args={'ylabel':'Vm (mV)', 'xlim':[0, data['t'][-1]]})
ge.plot(data['t'], Y=[data['Casoma'], data['Cadend']],
COLORS=['k', ge.blue], ax=AX[1],
axes_args={'ylabel':'[Ca$^{2+}$] (nM)', 'xlabel':'time (ms)', 'xlim':[0, data['t'][-1]]})
ge.scatter(data['Espike_times'], data['Espike_IDs'], color=ge.green,ax=AX[2],no_set=True,ms=3)
ge.scatter(data['Ispike_times'], data['Espike_IDs'].max()*1.2+data['Ispike_IDs'], color=ge.red,
ax=AX[2], no_set=True, ms=3)
ge.set_plot(AX[2], [], xlim=[0, data['t'][-1]])
fig.savefig('/home/yann/Desktop/trace.svg')
ge.show()
return Residual
if __name__ == '__main__':
import sys, os
from model import Model
from analyz.workflow.batch_run import GridSimulation
# if not os.path.isfile('data/passive-props.npz'):
# print('provide the ')
Model['qAMPA'] = 0. # NBQX in experiments
# loading data from previous calib !
passive = load_dict('data/passive-props.npz')
for key, val in passive.items():
Model[key] = val
# build the stimulation
stim = build_stimulation()
if len(sys.argv) == 1:
best_chelated_config = load_dict('data/best_chelatedZn_config.npz')
for key, val in best_chelated_config.items():
Model[key] = val
Npicked = compute_time_varying_synaptic_recruitment(
Model['Nsyn1'], Model['Nsyn2'], Model['Tnsyn20Hz'],
Model['Tnsyn3Hz'])
fig1, ax = vis.plot_segments(SEGMENTS['comp_type'] != 'axon',
bar_scale_args={},
fig_args=dict(figsize=(1.5, 2.4),
left=0,
bottom=0,
top=0,
right=0))
vis.add_dots(ax, PROX, 50, ge.orange)
vis.add_dots(ax, DIST, 50, ge.orange)
ge.show()
elif args.protocol == 'syn-number':
tstop, t0_stim = 300, 20
if args.plot:
data = load_dict(
os.path.join('data', 'branco-syn-number-%s.npz' % suffix))
fig, AX = ge.figure(axes=(1, 2),
figsize=(.7, 1),
right=4.,
hspace=0.1,
bottom=0.1)
ge.title(AX[0],
'%i to %i synapses (by 2)' % (N_PULSES[0], N_PULSES[-1]))
for synapses_loc, label, ax in zip([PROX, DIST], ['prox', 'dist'],
AX):
for i in range(len(data['v-%s' % label])):
ax.plot(data['t'],
data['v-%s' % label][i],
label=label,
color='k',
output = run_model(Model)
np.savez(os.path.join('data', 'calib', fn + '.npz'), **output)
elif sys.argv[1] == 'calib-analysis':
# EXPERIMENTAL VALUES
expRm, expCm = 45.89e6, 311.89e-12
sim = GridSimulation(os.path.join('data', 'calib', 'passive-grid.npz'))
sim_results = {'Rm': np.zeros(sim.N), 'Cm': np.zeros(sim.N)}
for index in range(int(sim.N)):
output = load_dict(
os.path.join('data', 'calib',
sim.params_filename(index) + '.npz'))
Rm, Cm = perform_ICcharact(1e-3 * output['t'],
1e-3 * output['Vm_soma'],
t0=50e-3,
t1=150e-3)
sim_results['Rm'][index], sim_results['Cm'][index] = Rm, Cm
# perform minimization
to_minimize = (1+np.abs(sim_results['Rm']-expRm)/sim_results['Rm'])*\
(1+np.abs(sim_results['Cm']-expCm)/sim_results['Cm'])
ibest = np.argmin(to_minimize)
# save results as default passive props
passive_props = {
'filename':
